InsightMapper: A closer look at inner-instance information for vectorized High-Definition Mapping

Zhenhua Xu, Kwan-Yee K. Wong, Hengshuang Zhao

Keywords: Vector map detection, computer vision, autonomous driving, robotics

Abstract: Vectorized high-definition (HD) maps contain detailed information about surrounding road elements, which are crucial for various downstream tasks in modern autonomous vehicles, such as motion planning and vehicle control. Recent works have attempted to directly detect the vectorized HD map as a point set prediction task, resulting in significant improvements in detection performance. However, these methods fail to analyze and exploit the inner-instance correlations between predicted points, impeding further advancements. To address this issue, we investigate the utilization of inner-$\textbf{INS}$tance information for vectorized h$\textbf{IGH}$-definition mapping through $\textbf{T}$ransformers and introduce InsightMapper. This paper presents three novel designs within InsightMapper that leverage inner-instance information in distinct ways, including hybrid query generation, inner-instance query fusion, and inner-instance feature aggregation. Comparative experiments are conducted on the NuScenes dataset, showcasing the superiority of our proposed method. InsightMapper surpasses previous state-of-the-art (SOTA) methods by 5.78 mAP and 7.03 TOPO, which assess topology correctness. Simultaneously, InsightMapper maintains high efficiency during both training and inference phases, resulting in remarkable comprehensive performance.

Primary Area: applications to robotics, autonomy, planning

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Submission Number: 6617